Fast computing of some generalized linear mixed pseudo-models with temporal autocorrelation

This paper considers ways to increase computational speed in generalized linear mixed pseudo-models for the case of many repeated measurements on subjects. We obtain linearly increasing computing time with number of observations, as opposed to O( n 3 ) increasing computing time using numerical optim...

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Vydáno v:Computational statistics Ročník 25; číslo 1; s. 39 - 55
Hlavní autoři: Ver Hoef, Jay M., London, Josh M., Boveng, Peter L.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer-Verlag 01.03.2010
Springer Nature B.V
Témata:
Algorithms
Computer science
Datasets
Economic Theory/Quantitative Economics/Mathematical Methods
Fisheries
Mathematical models
Mathematics and Statistics
Optimization
Original Paper
Parameter estimation
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Processing speed
Sparsity
Statistics
Studies
Time series
Sherman–Morrison–Woodbury
Pseudo-likelihood
Sparse matrix
Exponential autocorrelation
ISSN:0943-4062, 1613-9658
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Shrnutí:This paper considers ways to increase computational speed in generalized linear mixed pseudo-models for the case of many repeated measurements on subjects. We obtain linearly increasing computing time with number of observations, as opposed to O( n 3 ) increasing computing time using numerical optimization. We also find a surprising result; that incomplete optimization for covariance parameters within the larger parameter estimation algorithm actually decreases time to convergence. After comparing various computing algorithms and choosing the best one, we fit a generalized linear mixed model to a binary time series data set with over 100 fixed effects, 50 random effects, and approximately 1.5 ×  10 5 observations.
Bibliografie:SourceType-Scholarly Journals-1
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ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-009-0160-1